The aim of this study was toward the possibility of producing antigen that has the ability to stimulate the immune response against the infection with the hydatid cyst. To do so antigens were extracted from sheep hydatid cyst fluid of Echinococcus granulosus .These were: 1- The hydatid cyst fluid called antigen B. 2- Excretion-secretion called ES antigen. 3-B/ES antigen is a mixture (1:1) of the above two antigens. Three concentrations (15, 30 and 60 µg/ml) from antigen B/ES were prepared to immunize the white mice (males) with 20 µg/gr body weight and one booster dose (10 µg/gr) to stimulate immunity. The efficiency of these antigen concentrations against secondary infections was investigated by the calculation of the reduction in percentage of cysts numbers The results revealed that the reduction percentages of cyst in immunized group were 91.5% , 92.3% and 100% for concentration antigen 15, 30 and 60µg/g for Thr Immunized groups respectively.. There was a significant difference (p≤0.05) between and the positive control group infection. After 45 days of infection, the sensitivity test showed that the highest cellular immune response occurred after three hours of the injection of the B/ES antigen in the left footpad of the animals. This response was measured through the increase in the thickness of the footpad. The reaction was clear in the positive control and in the infected immunized animals. When the test was repeated after 90 days of the infection, the results were similar to the above, but the reaction was more acute than in the first time especially in the positive control groups of mice.
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There many methods for estimation of permeability. In this Paper, permeability has been estimated by two methods. The conventional and modified methods are used to calculate flow zone indicator (FZI). The hydraulic flow unit (HU) was identified by FZI technique. This technique is effective in predicting the permeability in un-cored intervals/wells. HU is related with FZI and rock quality index (RQI). All available cores from 7 wells (Su -4, Su -5, Su -7, Su -8, Su -9, Su -12, and Su -14) were used to be database for HU classification. The plot of probability cumulative of FZI is used. The plot of core-derived probability FZI for both modified and conventional method which indicates 4 Hu (A, B, C and D) for Nahr Umr forma
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This paper proposes a better solution for EEG-based brain language signals classification, it is using machine learning and optimization algorithms. This project aims to replace the brain signal classification for language processing tasks by achieving the higher accuracy and speed process. Features extraction is performed using a modified Discrete Wavelet Transform (DWT) in this study which increases the capability of capturing signal characteristics appropriately by decomposing EEG signals into significant frequency components. A Gray Wolf Optimization (GWO) algorithm method is applied to improve the results and select the optimal features which achieves more accurate results by selecting impactful features with maximum relevance
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Aspect-based sentiment analysis is the most important research topic conducted to extract and categorize aspect-terms from online reviews. Recent efforts have shown that topic modelling is vigorously used for this task. In this paper, we integrated word embedding into collapsed Gibbs sampling in Latent Dirichlet Allocation (LDA). Specifically, the conditional distribution in the topic model is improved using the word embedding model that was trained against (customer review) training dataset. Semantic similarity (cosine measure) was leveraged to distribute the aspect-terms to their related aspect-category cognitively. The experiment was conducted to extract and categorize the aspect terms from SemEval 2014 dataset.
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This search has introduced the techniques of multi-wavelet transform and neural network for recognition 3-D object from 2-D image using patches. The proposed techniques were tested on database of different patches features and the high energy subband of discrete multi-wavelet transform DMWT (gp) of the patches. The test set has two groups, group (1) which contains images, their (gp) patches and patches features of the same images as a part of that in the data set beside other images, (gp) patches and features, and group (2) which contains the (gp) patches and patches features the same as a part of that in the database but after modification such as rotation, scaling and translation. Recognition by back propagation (BP) neural network as
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This article aims to estimate the partially linear model by using two methods, which are the Wavelet and Kernel Smoothers. Simulation experiments are used to study the small sample behavior depending on different functions, sample sizes, and variances. Results explained that the wavelet smoother is the best depending on the mean average squares error criterion for all cases that used.